Reinforcement Agents for E-Learning Applications

Abstract

Advanced computer systems have become pivotal components for learning. However, we are still faced with many challenges in e-learning environments when developing reliable tools to assist users and facilitate and enhance the learning process. For instance, the problem of creating a user-friendly system that can learn from interaction with dynamic learning requirements and deal with largescale information is still widely unsolved. We need systems that have the ability to communicate and cooperate with the users, learn their preferences and increase the learning efficiency of individual users. Reinforcement learning (RL) is an intelligent technique with the ability to learn from interaction with the environment. It learns from trial and error and generally does not need any training data or a user model. At the beginning of the learning process, the RL agent does not have any knowledge about the actions it should take. After a while, the agent learns which actions yield the maximum reward. The ability of learning from interaction with a dynamic environment and using reward and punishment independent of any training data set makes reinforcement learning a suitable tool for e-learning situations, where subjective user feedback can easily be translated into a reinforcement signal.

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